Hybrid Sentiment Analysis Using Fine-Tuned Pre-Trained Language Models for Domain-Specific Insights and Cross-Industry Adaptability

Abstract

Through the development of user-generated content on online media, there has been an increased pressure to carry out a perfect sentiment analysis particularly in the specialized sectors. The sentiment analysis methodologies are not applied in practice to the domain specificity and smooth or expressed sentiment. In this paper, a hybrid sentiment analysis model which incorporates pre-trained language models (e.g., BERT and GPT) that combine support vector machines and random forests as a component of hybridization to improve performance and provide greater flexibility in terms of sentiment detection is presented. This paper is a systematic analysis of whether the hybrid model is able to estimate intensive sentiment or otherwise, this is achieved by comparing it to the traditional hybrid research design on different industries and perspectives. It also takes into account the fact that it may be generalized outside the small scope within which the measurements were regulated. The experimental findings of the given study will be more precise, versatile, and cross-implemented to fields. All these extended measures functions together ought to provide an all-encompassing strategy to sentiment-based decision-making in specific conditions to the researchers and professionals.